The solution space of genome-scale models of cellular metabolism provides a map between physically
viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the
corresponding growth rates. By sampling the solution space of E. coliʼs metabolic network, we show
that empirical growth rate distributions recently obtained in experiments at single-cell resolution can
be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the
higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of
a large bacterial population that captures this trade-off. The scaling relationships observed in
experiments encode, in such frameworks, for the same distance from the maximum achievable growth
rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being
grounded on genome-scale metabolic network reconstructions, these results allow for multiple
implications and extensions in spite of the underlying conceptual simplicity.